A Paratope-Enhanced Method to Determine Breadth and Depth TCR Clonal Metrics of the Private Human T-Cell Vaccine Response after SARS-CoV-2 Vaccination

Quantitative metrics for vaccine-induced T-cell responses are an important need for developing correlates of protection and their use in vaccine-based medical management and population health. Molecular TCR analysis is an appealing strategy but currently requires a targeted methodology involving complex integration of ex vivo data (antigen-specific functional T-cell cytokine responses and TCR molecular responses) that uncover only public antigen-specific metrics. Here, we describe an untargeted private TCR method that measures breadth and depth metrics of the T-cell response to vaccine challenge using a simple pre- and post-vaccine subject sampling, TCR immunoseq analysis, and a bioinformatic approach using self-organizing maps and GLIPH2. Among 515 subjects undergoing SARS-CoV-2 mRNA vaccination, we found that breadth and depth metrics were moderately correlated between the targeted public TCR response and untargeted private TCR response methods. The untargeted private TCR method was sufficiently sensitive to distinguish subgroups of potential clinical significance also observed using public TCR methods (the reduced T-cell vaccine response with age and the paradoxically elevated T-cell vaccine response of patients on anti-TNF immunotherapy). These observations suggest the promise of this untargeted private TCR method to produce T-cell vaccine-response metrics in an antigen-agnostic and individual-autonomous context.

Grouping T-Cell Antigen Receptors by Specificity

Grouping TCRs on the similarity of CDR3 sequences could effectively cluster them by specificity. Three versions of the GLIPH algorithm are described briefly here, with instructions to use GLIPH algorithms to cluster TCRs by specificity.

High-Throughput Immunogenetics Reveals a Lack of Physiological T Cell Clusters in Patients With Autoimmune Cytopenias

Autoimmune cytopenias (AIC) such as immune thrombocytopenia or autoimmune hemolytic anemia are claimed to be essentially driven by a dysregulated immune system. Using next-generation immunosequencing we profiled 59 T and B cell repertoires (TRB and IGH) of 25 newly diagnosed patients with primary or secondary (lymphoma-associated) AIC to test the hypothesis if these patients present a disease-specific immunological signature that could reveal pathophysiological clues and eventually be exploited as blood-based biomarker. Global TRB and IGH repertoire metrics as well as VJ gene usage distribution showed uniform characteristics for all lymphoma patients (high clonality and preferential usage of specific TRBV- and TRBJ genes), but no AIC-specific signature. Since T cell immune reactions toward antigens are unique and polyclonal, we clustered TCRβ clones in-silico based on target recognition using the GLIPH (grouping of lymphocyte interactions by paratope hotspots) algorithm. This analysis revealed a considerable lack of physiological T cell clusters in patients with primary AIC. Interestingly, this signature did not discriminate between the different subentities of AIC and was also found in an independent cohort of 23 patients with active autoimmune hepatitis. Taken together, our data suggests that the identified T cell cluster signature could represent a blood biomarker of autoimmune conditions in general and should be functionally validated in future studies.